{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "45111406-ed47-49b8-ade4-22877cb3313d",
   "metadata": {},
   "source": [
    "# NMF Topic Modeling\n",
    "\n",
    "* Author: \t\t\tValentina Gonzalez Rostani\n",
    "*  Contact mag384@pitt.edu\n",
    "* Date: August 19, 2024\n",
    "* Version: Python 3.8.17\n",
    "\n",
    "This jupyter notebook:\r\n",
    "\n",
    "- Createsinputs for Table A17: NMF Topic Modeling.\n",
    "- First it does the NMF for the US, then for Germany.\n",
    "- Once topic are created the proportion of each one of them is calculated. \n",
    "\n",
    "\n",
    "Input:\n",
    "\n",
    "- Data/cleaned_data.csv # Data for the US\n",
    "- Data/cleaned_data_G.csv # Data for Germany\n",
    "\n",
    "\n",
    "Output:\n",
    "\n",
    "- Table A17: NMF Topic Modeling, 4 clusters, top-10 terms. \t"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac569280-70c8-406f-895f-d1e8cd49f022",
   "metadata": {},
   "source": [
    "### Defining directory and importing libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f31c926f-8e9c-4574-b440-0894488142c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.datasets import fetch_20newsgroups\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.decomposition import NMF\n",
    "import os\n",
    "import pandas as pd\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1fce6cdd-4413-4471-8615-a2c7849f42c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Get the current working directory (where the Jupyter notebook is located)\n",
    "current_directory = os.getcwd()\n",
    "\n",
    "# Move one level up\n",
    "parent_directory = os.path.dirname(current_directory)\n",
    "\n",
    "# Change the working directory to the parent directory\n",
    "os.chdir(parent_directory)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b71ef513-68f9-462a-8875-a935ddd8038e",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Table A17 US part based on Trump Speeches\n",
    "\n",
    "### Loading data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "43473830-18ea-45dc-8843-85fd81f2a1e1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vgonz\\Dropbox\\Pitt\\OneDrive for Business\\Dissertation - Vale\\Paper 2 - Political-Economic Polarization\\Replication\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print(os.getcwd())\n",
    "# Read data into papers\n",
    "papers = pd.read_csv(\"Data/filtered_papers.csv\")\n",
    "\n",
    "papers.head()\n",
    "papers['text_new'] = papers['clean_text']\n",
    "\n",
    "papers = papers[['text_new']]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "29c6854b-8668-4bcc-a888-1e55015a961f",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# regular expression to remove any punctuation, and then lowercase the text\n",
    "# Load the regular expression library\n",
    "import re\n",
    "# Remove punctuation\n",
    "papers['text_new'] = \\\n",
    "papers['text_new'].map(lambda x: str(x))\n",
    "papers['text_new'] = \\\n",
    "papers['text_new'].map(lambda x: re.sub('[,\\.!?]', '', x))\n",
    "# Convert the titles to lowercase\n",
    "papers['text_new'] = \\\n",
    "papers['text_new'].map(lambda x: x.lower())\n",
    "\n",
    "# Print out the first rows of papers\n",
    "papers['text_new'].head()\n",
    "\n",
    "data = papers.text_new.values.tolist()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f2e21c6f-06a6-4730-993c-11733104ecc2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  (0, 157)\t0.2541362143925171\n",
      "  (0, 121)\t0.267210401562597\n",
      "  (0, 236)\t0.30608282861712227\n",
      "  (0, 84)\t0.29084662966495983\n",
      "  (0, 88)\t0.1605679036774965\n",
      "  (0, 160)\t0.3095009135181034\n",
      "  (0, 129)\t0.5752716648835042\n",
      "  (0, 230)\t0.19405504492725587\n",
      "  (0, 266)\t0.1965061630929149\n",
      "  (0, 357)\t0.31986792490495297\n",
      "  (0, 70)\t0.1693961300065119\n",
      "  (0, 193)\t0.16106520248676895\n",
      "  (1, 88)\t0.39752145846865616\n",
      "  (1, 129)\t0.7121062989633848\n",
      "  (1, 70)\t0.41937769078922194\n",
      "  (1, 193)\t0.3987526319686463\n",
      "  (2, 397)\t0.22335220056649369\n",
      "  (2, 29)\t0.2833958367387063\n",
      "  (2, 164)\t0.2935236635176798\n",
      "  (2, 30)\t0.28116618040007585\n",
      "  (2, 348)\t0.28455080751613276\n",
      "  (2, 306)\t0.2639623125653235\n",
      "  (2, 192)\t0.28694775536127126\n",
      "  (2, 85)\t0.253725926356122\n",
      "  (2, 54)\t0.44670440113298737\n",
      "  :\t:\n",
      "  (5705, 168)\t0.4111077452580616\n",
      "  (5705, 401)\t0.4288943921794285\n",
      "  (5705, 11)\t0.4370577993302784\n",
      "  (5705, 207)\t0.409901016420968\n",
      "  (5705, 24)\t0.23426900776262233\n",
      "  (5705, 487)\t0.36940383717648423\n",
      "  (5705, 319)\t0.21456556558036902\n",
      "  (5705, 88)\t0.2249935867439003\n",
      "  (5706, 277)\t0.8837739427797058\n",
      "  (5706, 319)\t0.46791411398205685\n",
      "  (5707, 474)\t0.75628101963952\n",
      "  (5707, 23)\t0.4544158311469677\n",
      "  (5707, 266)\t0.47068595871984387\n",
      "  (5708, 408)\t0.7223914720045738\n",
      "  (5708, 23)\t0.48027956413645023\n",
      "  (5708, 266)\t0.4974757295064418\n",
      "  (5709, 372)\t0.678826415933238\n",
      "  (5709, 23)\t0.5100169139414134\n",
      "  (5709, 266)\t0.5282778099872372\n",
      "  (5710, 175)\t0.5768138678764868\n",
      "  (5710, 23)\t0.5673717421389085\n",
      "  (5710, 266)\t0.5876861986193199\n",
      "  (5711, 44)\t0.6547316087516484\n",
      "  (5711, 426)\t0.46965600917769434\n",
      "  (5711, 168)\t0.5922412967235229\n",
      "X =  ['10' '100' '15' '20' '30' '33000' '35' '50' '8th' 'able' 'access'\n",
      " 'accomplish' 'according' 'actually' 'administration' 'african' 'agenda'\n",
      " 'ago' 'air' 'allow' 'allowed' 'amazing' 'amendment' 'america' 'american'\n",
      " 'americans' 'americas' 'anybody' 'anymore' 'appoint' 'ask' 'asking'\n",
      " 'attack' 'away' 'bad' 'badly' 'beautiful' 'beginning' 'believe' 'best'\n",
      " 'better' 'big' 'biggest' 'billion' 'bless' 'border' 'borders' 'break'\n",
      " 'bring' 'build' 'business' 'businesses' 'called' 'came' 'campaign' 'care'\n",
      " 'carolina' 'cash' 'chance' 'change' 'chicago' 'child' 'childcare'\n",
      " 'children' 'china' 'choice' 'cities' 'citizens' 'city' 'class' 'clinton'\n",
      " 'clintons' 'close' 'coal' 'come' 'coming' 'common' 'communities'\n",
      " 'community' 'companies' 'congress' 'constitution' 'control' 'core'\n",
      " 'corrupt' 'corruption' 'cost' 'countries' 'country' 'court' 'create'\n",
      " 'crime' 'criminal' 'cut' 'dangerous' 'day' 'days' 'dc' 'deal' 'deals'\n",
      " 'death' 'debate' 'debt' 'decades' 'defend' 'defense' 'deficit'\n",
      " 'democratic' 'democrats' 'department' 'detroit' 'did' 'didnt' 'different'\n",
      " 'disaster' 'disastrous' 'does' 'doesnt' 'doing' 'dollars' 'donald'\n",
      " 'donors' 'dont' 'dream' 'drugs' 'east' 'economic' 'economy' 'education'\n",
      " 'election' 'eliminate' 'emails' 'end' 'energy' 'enforcement' 'ensure'\n",
      " 'entire' 'especially' 'establishment' 'everybody' 'fact' 'factories'\n",
      " 'failed' 'failing' 'failures' 'families' 'family' 'far' 'fbi' 'federal'\n",
      " 'fight' 'fighting' 'fix' 'florida' 'folks' 'follow' 'force' 'foreign'\n",
      " 'forgotten' 'foundation' 'free' 'friends' 'future' 'gave' 'general'\n",
      " 'gets' 'getting' 'given' 'god' 'going' 'gone' 'gonna' 'good' 'got'\n",
      " 'government' 'great' 'greatest' 'growth' 'guy' 'half' 'hampshire'\n",
      " 'happen' 'happened' 'happening' 'hard' 'health' 'hear' 'heard' 'help'\n",
      " 'heres' 'hes' 'high' 'highest' 'hillary' 'hillarys' 'hispanic' 'history'\n",
      " 'home' 'honor' 'hope' 'horrible' 'house' 'hundreds' 'id' 'ill' 'illegal'\n",
      " 'im' 'imagine' 'immediately' 'immigrant' 'immigrants' 'immigration'\n",
      " 'important' 'includes' 'including' 'income' 'increase' 'incredible'\n",
      " 'infrastructure' 'inner' 'instead' 'interests' 'iran' 'iraq' 'isis'\n",
      " 'islamic' 'issue' 'ive' 'job' 'jobs' 'just' 'justice' 'justices' 'killed'\n",
      " 'killing' 'know' 'large' 'law' 'laws' 'leaders' 'leadership' 'leave'\n",
      " 'leaving' 'left' 'let' 'lets' 'libya' 'lie' 'lied' 'lies' 'life' 'like'\n",
      " 'live' 'lives' 'living' 'lobbyists' 'long' 'look' 'lose' 'lost' 'lot'\n",
      " 'love' 'low' 'lower' 'mails' 'major' 'make' 'making' 'man'\n",
      " 'manufacturing' 'massive' 'maybe' 'mean' 'means' 'media' 'men' 'mexico'\n",
      " 'michigan' 'middle' 'military' 'million' 'millions' 'money' 'movement'\n",
      " 'nafta' 'nation' 'national' 'nearly' 'need' 'new' 'nice' 'north'\n",
      " 'november' 'number' 'numbers' 'obama' 'obamacare' 'office' 'officers'\n",
      " 'oh' 'ohio' 'ok' 'old' 'open' 'opponent' 'opportunity' 'order'\n",
      " 'organization' 'outside' 'overseas' 'paid' 'parents' 'partnership'\n",
      " 'party' 'past' 'pay' 'paying' 'peace' 'pennsylvania' 'people' 'percent'\n",
      " 'person' 'place' 'places' 'plan' 'play' 'police' 'policies' 'policy'\n",
      " 'political' 'politicians' 'poverty' 'power' 'powerful' 'president'\n",
      " 'private' 'probably' 'problem' 'problems' 'prosperity' 'protect' 'proud'\n",
      " 'provide' 'public' 'radical' 'rate' 'real' 'really' 'reason' 'rebuild'\n",
      " 'reduce' 'reform' 'reforms' 'refugee' 'refugees' 'regulation'\n",
      " 'regulations' 'released' 'remember' 'renegotiate' 'repeal' 'replace'\n",
      " 'respect' 'rich' 'rid' 'rigged' 'right' 'roads' 'rules' 'run' 'running'\n",
      " 'russia' 'safe' 'safety' 'said' 'save' 'saw' 'say' 'saying' 'says'\n",
      " 'school' 'schools' 'second' 'secretary' 'security' 'seen' 'send' 'serve'\n",
      " 'server' 'set' 'share' 'shes' 'shot' 'signed' 'single' 'small' 'society'\n",
      " 'special' 'spent' 'stand' 'start' 'started' 'state' 'states' 'steel'\n",
      " 'stop' 'story' 'street' 'strong' 'students' 'suffering' 'support'\n",
      " 'supported' 'supreme' 'sure' 'syria' 'taken' 'taking' 'talk' 'talking'\n",
      " 'tax' 'taxes' 'tell' 'terrible' 'terrorism' 'terrorists' 'thank' 'thats'\n",
      " 'theres' 'theyre' 'theyve' 'thing' 'things' 'think' 'thousands' 'time'\n",
      " 'times' 'tired' 'today' 'total' 'totally' 'tough' 'tpp' 'trade'\n",
      " 'tremendous' 'trillion' 'true' 'trump' 'trying' 'turn' 'unbelievable'\n",
      " 'understand' 'united' 'use' 'used' 'veterans' 'victims' 'victory'\n",
      " 'violence' 'violent' 'voice' 'vote' 'voter' 'voters' 'voting' 'wages'\n",
      " 'waiting' 'wall' 'want' 'wants' 'war' 'washington' 'way' 'wealth'\n",
      " 'wealthy' 'went' 'weve' 'whats' 'white' 'win' 'winning' 'women'\n",
      " 'wonderful' 'wont' 'words' 'work' 'workers' 'working' 'world' 'worse'\n",
      " 'worst' 'wouldnt' 'wrong' 'year' 'years' 'yesterday' 'york' 'young'\n",
      " 'youre' 'youve']\n"
     ]
    }
   ],
   "source": [
    "vectorizer = TfidfVectorizer(max_features=500, min_df=5, stop_words='english')\n",
    "X = vectorizer.fit_transform(data)\n",
    "words = np.array(vectorizer.get_feature_names_out())\n",
    "\n",
    "print(X)\n",
    "print(\"X = \", words)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdd5f414-dc52-42cf-b572-52bc41a3746f",
   "metadata": {},
   "source": [
    "### 4 topics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f882d233-c6b5-45b2-accd-91e5d58d9de0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Topic 1: dont,im,know,want,jobs,new,country,american,people,going\n",
      "Topic 2: world,better,proud,going,wealthy,strong,safe,great,make,america\n",
      "Topic 3: donors,trade,obama,emails,secretary,wants,state,clintons,clinton,hillary\n",
      "Topic 4: incredible,good,love,today,want,everybody,great,god,bless,thank\n"
     ]
    }
   ],
   "source": [
    "nmf = NMF(n_components=4, solver='mu', init='nndsvda')\n",
    "W = nmf.fit_transform(X)\n",
    "H = nmf.components_\n",
    "\n",
    "# Use W to get the most likely topic for each sentence\n",
    "most_likely_topic = W.argmax(axis=1)\n",
    "\n",
    "# Add most_likely_topic as a new column to the papers dataframe\n",
    "papers['most_likely_topic'] = most_likely_topic\n",
    "\n",
    "\n",
    "for i, topic in enumerate(H):\n",
    "     print(\"Topic {}: {}\".format(i + 1, \",\".join([str(x) for x in words[topic.argsort()[-10:]]])))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddae3b36-60d0-4972-8975-8f2aa375bc77",
   "metadata": {},
   "source": [
    "### Topics proportions\n",
    "This method calculates the proportion of each topic based on the sum of the weights (or contributions) for each topic across all documents. It gives a sense of how much each topic contributes to the overall data set based on the NMF model's output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e0a9ad40-7b14-4795-85aa-ce0b967d1eb1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[t-SNE] Computing 121 nearest neighbors...\n",
      "[t-SNE] Indexed 5712 samples in 0.005s...\n",
      "[t-SNE] Computed neighbors for 5712 samples in 0.250s...\n",
      "[t-SNE] Computed conditional probabilities for sample 1000 / 5712\n",
      "[t-SNE] Computed conditional probabilities for sample 2000 / 5712\n",
      "[t-SNE] Computed conditional probabilities for sample 3000 / 5712\n",
      "[t-SNE] Computed conditional probabilities for sample 4000 / 5712\n",
      "[t-SNE] Computed conditional probabilities for sample 5000 / 5712\n",
      "[t-SNE] Computed conditional probabilities for sample 5712 / 5712\n",
      "[t-SNE] Mean sigma: 0.000000\n",
      "[t-SNE] KL divergence after 250 iterations with early exaggeration: 62.141548\n",
      "[t-SNE] KL divergence after 300 iterations: 1.653150\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "from sklearn.manifold import TSNE\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Apply t-SNE to reduce the dimensionality of the data\n",
    "tsne = TSNE(n_components=2, verbose=1, perplexity=40, n_iter=300)\n",
    "tsne_results = tsne.fit_transform(W)\n",
    "\n",
    "# Calculate the proportion of each topic\n",
    "topic_proportions = W.sum(axis=0) / W.sum()\n",
    "\n",
    "\n",
    "\n",
    "# Create a pie chart with the topic proportions\n",
    "fig, ax = plt.subplots(figsize=(8, 8))\n",
    "ax.pie(topic_proportions, labels=['Topic {}'.format(i+1) for i in range(len(topic_proportions))],\n",
    "       autopct='%1.1f%%', shadow=True, startangle=90)\n",
    "ax.set_title('Topic Proportions')\n",
    "\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a54c4da0-f2b4-48c9-a9fb-66a5c6e7e0bc",
   "metadata": {},
   "source": [
    "# Table A17 Geramany part based on AfD Manifesto\n",
    "\n",
    "### Loading data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3635a49f-9349-4133-a69e-1abaa86097ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read data into papers\n",
    "papers = pd.read_csv(\"Data/filtered_papers_G.csv\")\n",
    "\n",
    "# Print head\n",
    "papers.head()\n",
    "papers['text_new'] = papers['clean_text']\n",
    "\n",
    "papers = papers[['text_new']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8fc93b62-c8cc-475a-be71-3df5da86443e",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# regular expression to remove any punctuation, and then lowercase the text\n",
    "# Load the regular expression library\n",
    "import re\n",
    "# Remove punctuation\n",
    "papers['text_new'] = \\\n",
    "papers['text_new'].map(lambda x: str(x))\n",
    "papers['text_new'] = \\\n",
    "papers['text_new'].map(lambda x: re.sub('[,\\.!?]', '', x))\n",
    "# Convert the titles to lowercase\n",
    "papers['text_new'] = \\\n",
    "papers['text_new'].map(lambda x: x.lower())\n",
    "\n",
    "# Print out the first rows of papers\n",
    "papers['text_new'].head()\n",
    "\n",
    "data = papers.text_new.values.tolist()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e134d0a-6740-4c83-a3c7-c176ff06de50",
   "metadata": {},
   "source": [
    "### Vectorize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d18b3bd4-3035-4061-9b94-6e319b37f21c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  (0, 93)\t0.07174913158727336\n",
      "  (0, 141)\t0.08517716772192677\n",
      "  (0, 192)\t0.08062221260594189\n",
      "  (0, 28)\t0.14992129008730995\n",
      "  (0, 112)\t0.07330763289579756\n",
      "  (0, 85)\t0.0786015587106631\n",
      "  (0, 75)\t0.11044404142329659\n",
      "  (0, 130)\t0.12344523816910023\n",
      "  (0, 90)\t0.0786015587106631\n",
      "  (0, 37)\t0.08280468507616007\n",
      "  (0, 21)\t0.07496064504365497\n",
      "  (0, 59)\t0.08280468507616007\n",
      "  (0, 179)\t0.06506553689091266\n",
      "  (0, 169)\t0.07496064504365497\n",
      "  (0, 124)\t0.08280468507616007\n",
      "  (0, 106)\t0.16560937015232013\n",
      "  (0, 34)\t0.1572031174213262\n",
      "  (0, 150)\t0.08517716772192677\n",
      "  (0, 74)\t0.0786015587106631\n",
      "  (0, 166)\t0.08280468507616007\n",
      "  (0, 175)\t0.06887633490716778\n",
      "  (0, 44)\t0.14992129008730995\n",
      "  (0, 35)\t0.08280468507616007\n",
      "  (0, 116)\t0.05522202071164829\n",
      "  (0, 23)\t0.08517716772192677\n",
      "  :\t:\n",
      "  (88, 72)\t0.08947727669259041\n",
      "  (88, 60)\t0.08624589213964215\n",
      "  (88, 108)\t0.2645719464032926\n",
      "  (88, 170)\t0.06802572502537824\n",
      "  (88, 102)\t0.07387435634840024\n",
      "  (88, 47)\t0.06595541571635179\n",
      "  (88, 73)\t0.048581700363100805\n",
      "  (89, 83)\t0.24648434231513133\n",
      "  (89, 153)\t0.22745594884055537\n",
      "  (89, 41)\t0.20762650348631298\n",
      "  (89, 177)\t0.24648434231513133\n",
      "  (89, 80)\t0.23961888951556226\n",
      "  (89, 11)\t0.23330328516019314\n",
      "  (89, 123)\t0.19546356281130609\n",
      "  (89, 121)\t0.19179299716085516\n",
      "  (89, 125)\t0.15980061113160587\n",
      "  (89, 152)\t0.4242729399030788\n",
      "  (89, 45)\t0.5451197078575462\n",
      "  (89, 108)\t0.21691993039053534\n",
      "  (89, 154)\t0.21691993039053534\n",
      "  (90, 2)\t0.3314975196010503\n",
      "  (90, 123)\t0.2918564098205376\n",
      "  (90, 7)\t0.16430966336553682\n",
      "  (90, 62)\t0.8637687613051982\n",
      "  (90, 73)\t0.17842391113022094\n",
      "X =  ['able' 'according' 'account' 'act' 'addition' 'advocate' 'advocates'\n",
      " 'afd' 'aim' 'allow' 'appropriate' 'areas' 'authorities' 'based' 'basic'\n",
      " 'basis' 'better' 'care' 'case' 'central' 'chapter' 'children' 'citizens'\n",
      " 'civil' 'committed' 'common' 'comprehensive' 'conditions' 'constitution'\n",
      " 'constitutional' 'control' 'core' 'costs' 'countries' 'country' 'create'\n",
      " 'cultural' 'culture' 'current' 'currently' 'decision' 'demand' 'demands'\n",
      " 'democracy' 'democratic' 'development' 'does' 'economic' 'economy' 'end'\n",
      " 'ensure' 'environment' 'equal' 'established' 'eu' 'euro' 'europe'\n",
      " 'european' 'existing' 'families' 'family' 'favour' 'federal' 'financial'\n",
      " 'foreign' 'form' 'framework' 'free' 'freedom' 'fundamental' 'future'\n",
      " 'general' 'german' 'germany' 'good' 'government' 'high' 'immigration'\n",
      " 'income' 'increased' 'increasing' 'individual' 'influence'\n",
      " 'infrastructure' 'instead' 'institutions' 'integration' 'interests'\n",
      " 'international' 'labour' 'language' 'large' 'law' 'laws' 'lead' 'led'\n",
      " 'legal' 'legislation' 'level' 'life' 'limited' 'local' 'long' 'longer'\n",
      " 'low' 'lower' 'maintain' 'make' 'market' 'mass' 'means' 'measures'\n",
      " 'members' 'ment' 'migration' 'nation' 'national' 'nature' 'necessary'\n",
      " 'need' 'needs' 'new' 'non' 'number' 'open' 'order' 'parents' 'parliament'\n",
      " 'particular' 'parties' 'people' 'policies' 'policy' 'political'\n",
      " 'population' 'possible' 'power' 'prevent' 'principle' 'principles'\n",
      " 'private' 'protect' 'protection' 'public' 'quality' 'rates' 'reason'\n",
      " 'reasons' 'reduce' 'reduced' 'reform' 'regard' 'regulations' 'rejects'\n",
      " 'remain' 'required' 'requirements' 'respect' 'result' 'return' 'right'\n",
      " 'rights' 'role' 'schools' 'sector' 'security' 'self' 'services' 'set'\n",
      " 'shall' 'social' 'society' 'special' 'standards' 'state' 'states'\n",
      " 'strengthen' 'sufficient' 'supply' 'support' 'systems' 'taken' 'tax'\n",
      " 'term' 'time' 'tion' 'use' 'used' 'values' 'want' 'wants' 'way' 'welfare'\n",
      " 'whilst' 'years']\n"
     ]
    }
   ],
   "source": [
    "vectorizer = TfidfVectorizer(max_features=1500, min_df=10, stop_words='english')\n",
    "X = vectorizer.fit_transform(data)\n",
    "words = np.array(vectorizer.get_feature_names_out())\n",
    "\n",
    "print(X)\n",
    "print(\"X = \", words)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9255898d-22c0-4123-8db2-8aea8d37909e",
   "metadata": {},
   "source": [
    "### 4 topics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "684e2ade-a420-4762-a85b-2e0dce6439f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Topic 1: power,foreign,national,euro,policy,public,political,germany,afd,german\n",
      "Topic 2: foreign,culture,economy,nature,schools,infrastructure,protection,security,policy,chapter\n",
      "Topic 3: special,afd,social,support,schools,care,family,parents,families,children\n",
      "Topic 4: right,legal,germany,country,migration,social,eu,countries,integration,immigration\n"
     ]
    }
   ],
   "source": [
    "nmf = NMF(n_components=4, solver='mu', init='nndsvda')\n",
    "W = nmf.fit_transform(X)\n",
    "H = nmf.components_\n",
    "\n",
    "# Use W to get the most likely topic for each sentence\n",
    "most_likely_topic = W.argmax(axis=1)\n",
    "\n",
    "# Add most_likely_topic as a new column to the papers dataframe\n",
    "papers['most_likely_topic'] = most_likely_topic\n",
    "\n",
    "\n",
    "for i, topic in enumerate(H):\n",
    "     print(\"Topic {}: {}\".format(i + 1, \",\".join([str(x) for x in words[topic.argsort()[-10:]]])))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d6e0b43-0b11-4da7-9572-fdc00e518201",
   "metadata": {},
   "source": [
    "### Topics proportions\n",
    "This method calculates the proportion of each topic based on the sum of the weights (or contributions) for each topic across all documents. It gives a sense of how much each topic contributes to the overall data set based on the NMF model's output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a57b509d-c02a-4292-85e0-adfcb9f02f06",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[t-SNE] Computing 90 nearest neighbors...\n",
      "[t-SNE] Indexed 91 samples in 0.000s...\n",
      "[t-SNE] Computed neighbors for 91 samples in 0.100s...\n",
      "[t-SNE] Computed conditional probabilities for sample 91 / 91\n",
      "[t-SNE] Mean sigma: 0.087386\n",
      "[t-SNE] KL divergence after 250 iterations with early exaggeration: 47.005867\n",
      "[t-SNE] KL divergence after 300 iterations: 0.064748\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.manifold import TSNE\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Apply t-SNE to reduce the dimensionality of the data\n",
    "tsne = TSNE(n_components=2, verbose=1, perplexity=40, n_iter=300)\n",
    "tsne_results = tsne.fit_transform(W)\n",
    "\n",
    "# Calculate the proportion of each topic\n",
    "topic_proportions = W.sum(axis=0) / W.sum()\n",
    "\n",
    "\n",
    "\n",
    "# Create a pie chart with the topic proportions\n",
    "fig, ax = plt.subplots(figsize=(8, 8))\n",
    "ax.pie(topic_proportions, labels=['Topic {}'.format(i+1) for i in range(len(topic_proportions))],\n",
    "       autopct='%1.1f%%', shadow=True, startangle=90)\n",
    "ax.set_title('Topic Proportions')\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.17"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
